Development and validation of DNA methylation scores in two European cohorts augment 10-year risk prediction of type 2 diabetes

Yipeng Cheng, Danni A. Gadd, Christian Gieger, Karla Monterrubio-Gómez, Yufei Zhang, Imrich Berta, Michael J. Stam, Natalia Szlachetka, Evgenii Lobzaev, Nicola Wrobel, Lee Murphy, Archie Campbell, Cliff Nangle, Rosie M. Walker, Chloe Fawns-Ritchie, Annette Peters, Wolfgang Rathmann, David J. Porteous, Kathryn L. Evans, Andrew M. McIntoshTimothy I. Cannings, Melanie Waldenberger, Andrea Ganna, Daniel L. McCartney, Catalina A. Vallejos (Lead / Corresponding author), Riccardo E. Marioni (Lead / Corresponding author)

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)


Type 2 diabetes mellitus (T2D) presents a major health and economic burden that could be alleviated with improved early prediction and intervention. While standard risk factors have shown good predictive performance, we show that the use of blood-based DNA methylation information leads to a significant improvement in the prediction of 10-year T2D incidence risk. Previous studies have been largely constrained by linear assumptions, the use of cytosine–guanine pairs one-at-a-time and binary outcomes. We present a flexible approach (via an R package, MethylPipeR) based on a range of linear and tree-ensemble models that incorporate time-to-event data for prediction. Using the Generation Scotland cohort (training set ncases = 374, ncontrols = 9,461; test set ncases = 252, ncontrols = 4,526) our best-performing model (area under the receiver operating characteristic curve (AUC) = 0.872, area under the precision-recall curve (PRAUC) = 0.302) showed notable improvement in 10-year onset prediction beyond standard risk factors (AUC = 0.839, precision–recall AUC = 0.227). Replication was observed in the German-based KORA study (n = 1,451, ncases = 142, P = 1.6 × 10−5).

Original languageEnglish
Pages (from-to)450-458
Number of pages9
JournalNature Aging
Issue number4
Publication statusPublished - 6 Apr 2023


  • Ageing
  • DNA methylation
  • Machine learning
  • Predictive markers

ASJC Scopus subject areas

  • Ageing
  • Geriatrics and Gerontology
  • Neuroscience (miscellaneous)


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